Interpretive Summary: Agricultural professionals, in both the private and public sectors, frequently use computer programs as tools to assist farmers in making crop and conservation management decisions. These computer programs use complex mathematical equations (referred to as models) that attempt to predict the crop production and environmental outcomes from a variety of management options. The land-use plans derived from computer program tools will only be as effective and profitable as the accuracy of the program model's predictions. This paper describes efforts to verify the ability of the Crop-Denitrification/Decomposition (Crop-DNDC) model to accurately predict corn and soybean grain yields across many different locations within a field. The Crop-DNDC model predicted yields were strongly influenced by the level of organic matter in the top two inches of soil for a given location, and did not adequately account for the influences of terrain on crop yield. To improve the Crop-DNDC model's predictive abilities, we recommend that efforts be made to include the soil organic matter content to the one-foot depth and to incorporate the relative elevation of locations within a field. These results are important to agricultural professionals by notifying them that this computer model is not yet advanced enough to use as a reliable tool to help guide crop and conservation management decisions.

Technical Abstract:
The global human population is projected to increase substantially over the next 50 years (from 6 to about 9 billion). This increase will put additional pressure on limited natural resources, particularly on soil nutrients and water, which are necessary inputs to food production. Precision agriculture offers the promise of reducing undesirable environmental impacts of intensive farming while maintaining or even enhancing crop yield. This will, however, only be possible if field management is based on accurate information with sufficient spatial and temporal resolution to account for within-field heterogeneity. In this study we examined the connection between the input data and performance of simulation model Crop Denitrification-Decomposition (DNDC). We adopted the DNDC model to a U.S. Midwestern corn-soybean farm field that has had intensive spatial and temporal sampling for crop and soil parameters. Comparison of real and simulated yields showed that real productivity of the field is strongly dependent on the factors of topographic position of each location and growing season climate, whereas, the model's simulation results were mainly determined by the soil organic carbon content (to the 5 cm depth) at each location. Future improvement of DNDC should be realized by efforts to incorporate the effect of topography and climate interactions on crop yield, plus taking into account soil organic carbon content deeper into the soil profile.